Smoothing of a digital image can be effectively implemented by averaging all pixels in a rectangular (box) neighborhood of a region (mask). This is known in the art as box filter. The box filter method runs independent of the box (filter kernel) size. However, the performance optimization of the box filter is based on the box shaped filter kernel. If a different shape filter kernel required smoothing, a 2-dimensional convolution has to be employed. This, however, takes longer time because of the increased calculations and the performance is dependent on the filter kernel size. The larger the filter kernel the longer it will take to smooth the digital image.
Typically, in a single photon emission computed tomography (SPECT), an iterative image reconstruction method uses models for a point-spread function (PSF) to improve spatial resolution in the reconstructed images. In order to accurately replicate the physical response of a collimator of the SPECT, the PSF models often depend on a bore shape of the collimator channels and distance between the collimator and an object. The bore shape of the collimator channels can be circular, rectangular, or hexagonal. The PSF models are depth dependent with the extent of the modeling function widening two (2) dimensions (e.g., width and height of the detected area) as the distance of the object from the collimator increases. The most time-consuming part of the iterative reconstruction is the computations to model the PSF or convolutions with the distance dependent bore shaped uniform convolution kernel.